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Seizures: Classification

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:

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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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Classifying cross-frequency coupling pattern in epileptogenic tissues by convolutional neural network^.

Zeyu Wang, Chunsheng Li

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|October 6, 2020

Summary

This study introduces a novel method using convolutional neural networks (CNNs) to identify epileptogenic tissue by analyzing cross-frequency coupling (CFC) patterns in electroencephalography (EEG) signals, achieving high accuracy.

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Phase-amplitude coupling in EEG signals is a potential biomarker for epileptogenic tissues.
Existing cross-frequency coupling (CFC) analyses often focus on coupling strength, not frequency-frequency patterns.
Physiological processes like memory can also generate phase-amplitude coupling, necessitating precise pattern analysis.

Purpose of the Study:

To develop and validate a novel method for identifying epileptogenic tissue using convolutional neural networks (CNNs).
To analyze cross-frequency coupling (CFC) patterns in the frequency-frequency domain for improved epilepsy localization.
To assess the efficacy of CNNs in distinguishing between epileptogenic and non-epileptogenic brain tissue based on SEEG data.

Main Methods:

Calculated modulation indexes (MIs) using a moving window approach on stereo-electroencephalograph (SEEG) data from six epilepsy patients.
Trained a convolutional neural network (CNN) on two-dimensional CFC patterns, labeling data as inside or outside the epileptogenic zone (EZ).
Validated the CNN model using a leave-one-out cross-validation strategy and generated receiver operating characteristic (ROC) curves.

Main Results:

The CNN model achieved an average area under the curve (AUC) of 0.88.
The model demonstrated a sensitivity of 0.81 and a specificity of 0.79 in identifying epileptogenic tissue.
Results indicate that CFC patterns are effective indicators for localizing SEEG channels within the epileptogenic region.

Conclusions:

Cross-frequency coupling (CFC) patterns, when analyzed with CNNs, can effectively identify epileptogenic brain regions.
This CNN-based approach shows significant potential as an analytical tool for neurologists in epilepsy surgery planning.
The method offers a promising advancement in the precise delineation of epileptogenic tissues using SEEG data.